The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.
The technology disclosed provides a so-called machine learned conversion optimization (MLCO) system that uses evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Website funnels with a single webpage or multiple webpages are represented as genomes. Genomes identify different dimensions and dimension values of the funnels. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well. Each webpage is tested only to the extent that it is possible to decide whether it is promising, i.e., whether it should serve as a parent for the next generation, or should be discarded.
Common methods for running controlled experiments on websites include sophisticated conversion optimization solutions. Conversion optimization includes testing multiple combinations and variations of webpages and page elements at the same time. For example, two alternative images, plus two alternative headlines, plus two copy text alternatives, for a total of twenty-seven possible combinations (including the original control versions) may be provided. Thus, conversion optimization introduces a rather complex set of permutations and combinations that need to be analyzed to determine the most effective combination of page elements that truly engage the users.
As Big Data plays a more important role in web personalization, the number of data signals, the complexity of rules, and the sheer number of outcomes has increased exponentially. As that happens, human optimization simply cannot be done except perhaps after the fact, where there is little to no opportunity to impact the outcome. Algorithmic optimization is required, but even there, simple linear regression algorithms that can handle linear relationships and correlations may not be able to sufficiently create improved outcomes, given the vast number of data inputs and resulting measurements that have to be processed to predict performance.
Machine learning systems are utilized to run tests where many variables with very complex relationships between them are involved in determining outcomes. Machine learning systems typically attempt to learn from the data to figure out the formula, rather than to try to figure out a formula to begin with, given that the relationships between the variables may be too complex to determine the algorithm in advance. Therefore, with so many variables at play in conversion optimization, very sophisticated algorithms are desirable that utilize machine learning, artificial intelligence, and other non-linear algorithms to make predictions about outcomes based on learning from large data sets of inputs.